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<h1 id="product">LingPipe</h1><h1 id="pagetitle">Download Models</h1>
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<h2>License</h2>

<h3>LingPipe License</h3>
<p>
Before downloading, please read the terms of:
</p>
<ul>
<li>
<a href="licensing.html"
 >LingPipe's Royalty-Free License</a>
</li>
</ul>

<p>
LingPipe is also available under a
<a href="licensing.html">commercial license</a>.
</p>

<h3>Model Licenses</h3>
<p>
Note that the models themselves are often subject to additional
licensing restrictions inherited from the corpora from which
they were trained.  These restricted models are themselves
often available for commercial purposes by contract to the original
creator of the data, or through Alias-i. All models, to the best of
our knowledge, allow research use.
</p>


<h2>Download LingPipe</h2>

<p>
In order to run these models, you must download LingPipe:
</p>
<ul><li>
<a href="download.html">LingPipe Core Download Page</a>
</li></ul>
<p>
This includes a compiled jar file, complete source, javadoc, demos, etc..
</p>

<h2>Where to Put Models</h2>
<p>
Models should be downloaded to the following directory
in order to ensure the demos that require them may find them:
</p>
<pre class="code">
$lingpipe/demos/models
</pre>



<h2>Download LingPipe Models</h2>



<div class="sidebar">
<h2>What are Models?</h2>
<p>
For each language, genre, and training corpus,
LingPipe is able to generate a range of models
based on task (e.g. named-entity recognition or
part-of-speech tagging) and training parameters.
These models are then loaded at run time by LingPipe
where they provide language-, genre- and task-specific
details to the core LingPipe API.
</p>
</div>


<div class="sidebar">
<h2>Model Naming Convention</h2>
<p>
The models are all serialized objects.  A name
such as <code>ne-en-news-muc6.TokenShapeChunker</code>
indicates that the task is named-entity recognition
(<code>ner</code>), the language is English (<code>en</code>),
the genre of text it applies to is news (<code>news</code>),
and the corpora on which it was trained is the MUC-6 corpus
(<code>muc6</code>).  The file suffix is the (unqualified)
name of the class of object represented in the model.
</p>
</div>

<h3>Part of Speech Tagging</h3>

<h4>English Biomedical: GENIA</h4>
<ul>
<li>
<a href="../demos/models/pos-en-bio-genia.HiddenMarkovModel"
>pos-en-bio-genia.HiddenMarkovModel</a>
<span class="smallnote">(5.9MB)</span>
</li>
</ul>

<h4>English Biomedical: MedPost</h4>
<ul>
<li>
<a href="../demos/models/pos-en-bio-medpost.HiddenMarkovModel"
>pos-en-bio-medpost.HiddenMarkovModel</a>
<span class="smallnote">(4.9MB)</span>
</li>
</ul>

<h4>English General: Brown</h4>
<ul>
<li>
<a href="../demos/models/pos-en-general-brown.HiddenMarkovModel"
>pos-en-general-brown.HiddenMarkovModel</a>
<span class="smallnote">(12.5MB)</span>
</li>
</ul>

<h3>Named Entity Recognition</h3>

<h4>English News: MUC-6</h4>
<ul>
<li>
<a href="../demos/models/ne-en-news-muc6.AbstractCharLmRescoringChunker"
>ne-en-news-muc6.AbstractCharLmRescoringChunker</a>
<span class="smallnote">(16.0MB)</span>
</li>
</ul>

<h4>English Genes: GeneTag</h4>
<ul>
<li>
<a href="../demos/models/ne-en-bio-genetag.HmmChunker"
>ne-en-bio-genetag.HmmChunker</a>
<span class="smallnote">(6.7MB)</span>
</li>
</ul>

<h4>English Genomics: GENIA</h4>
<ul>
<li>
<a href="../demos/models/ne-en-bio-genia.TokenShapeChunker"
>ne-en-bio-genia.TokenShapeChunker</a>
<span class="smallnote">(9.8MB)</span>
</li>
</ul>


<h3>Chinese Word Segmentation</h3>

<h4>Academia Sinica Version SIGHAN 2005</h4>
<ul>
<li>
<a href="../demos/models/words-zh-as.CompiledSpellChecker"
>words-zh-as.CompiledSpellChecker</a>
<span class="smallnote">(30.5MB)</span>
</li>
</ul>
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